“Big data is not defined by size―instead, it’s undeniably defined by a mindset. It’s the mindset to turn mess into meaning.”

Andreas Weigend, PhD, former Chief Scientist at Amazon.com and current director of the Social Data Lab, recently shared his insights on the untapped power of data with the audience at Technology Services World (TSW) Service Transformations in Las Vegas. Specifically he addressed what we can do to turn our vast amounts of big data into vital business decisions. What follows are highlights from his spot-on and dynamic look into the future of big data.

(Really) Big Data

Weigend opened up his discussion with a pointed and illuminating fact about the enormity of our data:

In 2012, we generated more data than mankind has generated from its beginnings through the year 2010.

Wrapping our heads around that concept seems nearly impossible. But Weigend believes that big data is not defined by size―instead, it’s undeniably defined by a mindset.

Tech has experienced notable progression over the last four-plus decades. The ’70s focused on building computers; the ’80s focused on connecting computers; the ’90s focused on connecting pages; the 2000s focused on connecting people; and it’s no surprise that the 2010s are about connecting data.

What’s key, says Weigend, is not about the insights we gain with regard to our data―it’s about the actions we take based on our data. “This big data revolution has reached a billion people,” he explains. “Amazon has changed the way a billion people think about what they buy. Google has changed the way a billion people think about information. Facebook has changed the way a billion people think about who they are and how they relate to others.”

Weigend’s definition of big data is this: It’s the mindset to turn mess into meaning. Taking a look at raw geolocation data, as Google knows it, it looks much like code, and to us non-techies out there, it can appear relatively messy and useless. However when it’s properly rendered, it gives us the location data we commonly see and use every day.

Weigend moved on to pose a great question to the audience:

If you had permissible, unrestricted access to all data available―from your systems, your customers’ systems, and your customers’ customers’ systems―what amazing service or irresistible product would you develop?

He gives a great example of Microsoft spinoff, INRIX, which collects traffic information and uses it predictively. “[INRIX] has customers you might not expect,” says Weigend. “For instance, Wall Street. [Taking a snapshot of the traffic data going] into the parking lot of Big Bucks retailers, and comparing that to the year before, allows them to make pretty good predictions of what’s going to happen to the stock price.”

Amazon’s Journey

Amazon, the world’s largest online retailer, has had a 20-year journey that’s been downright remarkable and heavily focused on providing excellent customer service. Weigend shared some stories on what’s changed during the Amazon journey, and what has not changed.

One thing that’s changed is the application of mobile. Mobile has allowed for consumers to scan barcodes and snap photos in order to search for specific products on Amazon―something that was unavailable 10 years ago. A deep-dive into this type of data allows Amazon to gather relevant supplementary information about how we consume. Another change was the focus from algorithms to data, and the increasing focus on data intelligence.

“What has not changed at Amazon is the mindset,” says Weigend. “Jeff [Bezos, Amazon founder and CEO] always says, ‘Ask for forgiveness; not for permission.’ And there’s actually the “Just Do It” award, which he gives out every few months … to somebody who did the wrong thing by the books, but the right thing for the company.” Weigend believes that the culture of innovation keeps going strong by adhering to this very mindset. Another thing that Weigend says has not changed at Amazon: true customer centricity.

The Equation of Your Business = Recipe

Weigend’s next point focused on understanding and documenting the equations of your business, and then combining them or changing them up―much like one would do in a cooking recipe in order to suit a person’s tastes or health needs. “Write down the various parts that make your business strategy explicit―to make the implicit parts explicit―and then engineer toward that equation,” Weigend advises. “Try to combine [your various data sources] into one equation, and then try to understand what the effects are if you brutally optimize for that equation.”

Weigend strongly believes that we must think about the short-term effects of changing up our “recipe” (e.g., adding more sugar makes for sweeter results in the short term) vs. the long-term effects (e.g., it sure tasted delicious at the moment, but now I have an extra pound or two to lose). Amazon tries to model the delight of the customer, and then takes a close look at the degree to which they got it so right. Experimenting with the data, and then incorporating modifications where needed based on making the end result better for the customer, is the essence of the innovation that companies like Amazon, Google, and Facebook have going on.

Recommendations

Figure 1 illustrates how Amazon managed to keep things simple through the use of data. The five stages below show the various data sources that worked together to form what we know as “recommendations,” as we’ve all seen on the web.

Figure 1

Our ability to allocate our resources more efficiently has never been so great. The customer identity, which used to be determined by a set of attributes, is now being determined by the relationships we are building with them. Weigend says we’ve moved from “Me” to “E” to “We” (see Figure 2).

Figure 2

Google Glass

“There’s no talk these days without Google Glass,” says Weigend, citing a partnership he’d recently heard of between Intuit and Google, whereby Glass will tell consumers the available balance in their checking account as they are considering a purchase. One of the amazing things about Glass for service people is that every person wearing Glass acts as a data collector for Google.

Give Data to Get Data

Weigend says that the mindset underlying this year is: Give data to get data. With that, he poses these two questions for us to consider:

Do your customers understand the value they getwhen they give you data (e.g., do they understand the value of inputting their e-mail address online)?

Does your product or service get better over time and with data, or worse?

In closing, Weigend summarized some rules to consider applying in this world of big data:

Start with a problem, not with the data.

Give data to get data.

Focus on metrics that matter to your customers.

Focus on actions and feedback; not on analytics and reports.

Let people do what people are good at, and let computers do what computers are good at.

To hear more about Weigend’s approach to big data, go here to watch his keynote in its entirety. And check back here to the Inside Technology Services blog this Thursday to read about the great Q&A that took place at TSW between Weigend and J.B. Wood, TSIA CEO and co-author of B4B―the newly launched must-read business book that discusses the implications of big data in-depth.

TSIA staff would like to take this opportunity to wish you a very happy, healthy, and prosperous 2014!

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